Quant's reading list: Derivative Pricing

This post is part of a series of book recommendations for novice quantitative analysts. Other articles in this series will focus on C++ programming, mathematical methods, and Python programming.

A list of books for beginner quantitative analysts

It seems there is no text provided for translation. Please share the text you'd like me to translate. This post is part of a list of books for beginner quantitative analysts, with other posts in this series focusing on C++ Programming, Numerical Methods, and Python Programming.

• Quant Reading List - Derivative Pricing
•  Quant Reading List - C++ Programming
•  Quant Reading List - Numerical Methods
•  Quant Reading List - Python Programming

Not everyone wants to be a physicist. Theory: Some people view academia as too comfortable. Others may not like the politics in the field or have to chase research funding from the start of their careers. Working in quantitative finance is an interesting alternative.

Financial engineering has both strong theoretical and applied components, is intellectually stimulating, and operates at a high speed. To secure a job interview, one must have essential foundational knowledge and an excellent academic record. If you have just decided that academia is not your career path and possess strong technical skills, the list of books mentioned below will help you start your journey to becoming a quantitative analyst (quant).

This is the first part of a multi-part series of books suitable for starting as a quantitative analyst. The next part will focus on applications, mathematical elaborations, interview skills, and mathematical methods. This article will emphasize the theory of financial engineering for those who have never encountered finance before.

Mathematical Finance

A good starting point for learning about derivatives is the classic book "Options, Futures, and Other Derivatives, 9th Edition" by John Hull. This book focuses on general content about various financial instruments and is relatively light on mathematics, making it suitable for introducing the derivatives market to those without a financial background.

Once you understand the concepts used in the financial industry, the next step for a novice quantitative analyst is to learn more about arbitrage and the Black-Scholes model in detail. The book "A Primer For The Mathematics Of Financial Engineering, 2nd Edition" by Dan Stefanica provides all the necessary mathematical theories, such as differentiation, integration, and Taylor expansions, to handle the Black-Scholes equation. It also covers "the Greeks" and risk-neutral pricing. This book is suitable for those who do not have the undergraduate-level mathematical background necessary for reading the next book.

During this period, you will be ready to read more challenging books, such as The Concepts and Practice of Mathematical Finance by Mark Joshi, which is an excellent book recommended by QuantStart. Another popular choice among many is Paul Wilmott Introduces Quantitative Finance, 2nd Edition, which is a comprehensive book that explains various concepts in mathematical finance with humor.

To enhance additional knowledge Financial Calculus: An Introduction to Derivative Pricing by Martin Baxter and Andrew Rennie (commonly known as 'Baxter and Rennie') And An Introduction to the Mathematics of Financial Derivatives, 3rd Edition by Salih Neftci is also a must-have book. Reading and understanding the content of these books will provide you with sufficient theory for job interviews at the front office of an organization.

If you want to study deeper mathematical theories in the field of derivative pricing, the book "Stochastic Differential Equations: An Introduction with Applications, 6th Edition" by Bernt Oksendal is a good starting point, as it contains many exercises on stochastic differential equations.

For master's students in Financial Engineering (MFE) and volatility desk quants, the two-volume set by Steven Shreve - Stochastic Calculus for Finance (Vol I: The Binomial Asset Pricing Model and Vol II: Continuous-Time Models) is indispensable. The first volume focuses on discrete pricing models, while the second volume emphasizes continuous models. However, it should be noted that the second volume requires a strong undergraduate mathematical foundation, particularly in Real Analysis, Probability Theory, and Measure Theory.

Reference: Quant Reading List Derivative Pricing

From https://www.quantstart.com/articles/Quant-Reading-List-Derivative-Pricing/